| CODE | SOR5211 | ||||||||||||
| TITLE | Multivariate Analysis 2 | ||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||
| MQF LEVEL | 7 | ||||||||||||
| ECTS CREDITS | 10 | ||||||||||||
| DEPARTMENT | Statistics and Operations Research | ||||||||||||
| DESCRIPTION | Students taking this study-unit will be assigned a number of topics selected from the ones below which would cover topics of direct use to the particular student involved and which would make study-time demands commensurate with the number of credits being allotted: - Multivariate Distributions; - Estimation in the Multivariate Setting; - Hypothesis Testing in the Multivariate Setting; - Principal Component Analysis; - Factor Analysis; - Discriminant Analysis; - Correspondence Analysis; - Cluster Analysis; - MANOVA; - Multidimensional Scaling; - Applications in the Multivariate Setting. Study-Unit Aims: Starting off with a sound background from the earlier topics in mathematical statistics and limits in probability, this study-unit provides first a solid framework for generalization concepts and major statistical and relevant probabilistic results from the unidimensional to the multidimensional setting. Subsequently it offers, at a level commensurate with postgraduate standards, a number of classical areas of study within multivariate analysis considered to be building blocks on which statistical theory for widely used techniques in the multidimensional setting is based. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: a) Have a deep understanding of how the modern theory of multivariate statistics has developed; b) Comprehend the fundamental properties and major results of the types of statistical reasoning, concepts and results within the multidimensional setting studied with an emphasis on the mathematical subtleties involved. 2. Skills: By the end of the study-unit the student will be able to: a) Use knowledge of multivariate analysis to analyze problems involving statistics of vector-valued readings; b) Know which results would be suitable to use in particular contexts; c) Be in a position to know how the various types of statistical techniques can be used to model diverse practical situations; d) Be able to appreciate the limitations in applying multivariate theory to situations where the basic assumptions of the theory may not be satisfied in full; e) Use specific software to solve practical problems. Main Text/s and any supplementary readings: - Mardia, K.V., Kent, J.T. and Bibby, J.M. (1995) Multivariate Analysis, Academic. - Johnson, R.A. and Wichern, D.W. (2007) Applied Multivariate Statistical Analysis, Prentice Hall. - G. A. F. Seber, (2003) Linear Regression Analysis, Wiley. - Srivastava, M.S. and Khatri, C.G. (1983) An Introduction to Multivariate Statistics, North Holland. - Flury, B. (1997) A First Course in Multivariate Statistics, Springer Hair J., Anderson R., Tatham R., Black W., (1998) Multivariate Data Analysis, Prentice Hall. - Rencher A., (2002) Methods of Multivariate Analysis, Wiley. - Kendall, M. G. (1980) Multivariate analysis. (2nd ed.) London: Griffin. |
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| ADDITIONAL NOTES | Pre-requisite Study-units: SOR2211, SOR2221, SOR2120, SOR3210 & SOR3221 | ||||||||||||
| STUDY-UNIT TYPE | Independent Study | ||||||||||||
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years. |
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